Lecture 1: Introduction

Economics 326 — Methods of Empirical Research in Economics

Author

Vadim Marmer, UBC

What is econometrics?

Econometrics develops statistical methods for:

Estimating economic relationships
Testing economic theories
Forecasting important economic variables
Evaluating government and business policy

Why statistics?

Economic theory motivates models of relationships between variables of interest.
Economic models are approximations, not exact descriptions of reality.
Even good models omit important factors that affect outcomes.
We replace a deterministic model with a probabilistic model.

Examples

Estimation of demand and supply functions
Elasticities help evaluate the effects of taxation.
Mincer (1974), Schooling, Experience, and Earnings
Uses individual data to estimate returns to schooling and experience.
- Determine an “optimal” amount of schooling
- Study education in developing countries
- Study gender and race discrimination
- Study the impact of immigration on labour markets
Paarsch (1997), Journal of Econometrics
Estimates optimal reserve prices for BC timber auctions.
Chandra et al. (2008), Pediatrics
Studies how exposure to sexual content on TV relates to teen pregnancy.

Types of data: cross-section

Definition

A cross-sectional dataset contains observations on individuals (e.g., workers or firms) collected in a single time period.

Example (wages and individual characteristics):

obs	wage	education	experience	female	married
1	3.10	11	2	1	0
2	3.24	12	22	1	1
3	3.00	11	2	0	0
…	…	…	…	…	…

The order of observations is not important.
It is often reasonable to assume observations are statistically independent.

Types of data: time series

Definition

A time series dataset contains observations on one or more variables over time.

Example (Puerto Rico minimum wage, unemployment, and GNP):

obs	year	minimum wage	unemployment	gnp
1	1950	0.20	15.4	878.7
2	1951	0.21	16.0	925.0
3	1952	0.23	14.8	1015.9
…	…	…	…	…

Data frequency can be daily/weekly/monthly/quarterly/annual; in finance, trade data can be very high frequency.
The order of observations is important.
Observations are often correlated (e.g., trends).

Types of data: panel

Definition

A panel dataset combines cross-section and time series: a time series for each cross-sectional unit.

Example (two-year panel on city crime):

obs	city	year	murders	population	unempl	police
1	1	1986	5	350000	8.7	440
2	1	1990	8	359200	7.2	471
3	2	1986	2	64300	5.4	75
4	2	1990	1	65100	5.5	75
…	…	…	…	…	…	…

Causality

We care about causal relationships, but data often only reveal correlations (associations).
To claim a causal effect, other factors affecting the outcome must be held fixed (controlled for).
Controlled experiments help with causality in the natural sciences.
Experiments are often impossible in economics (cost and ethics).
We typically work with observational data.

Examples (causality)

Education

\log(\text{Wage}) = \alpha + \beta \times \text{Years of Schooling} + U

U includes other factors (e.g., ability). If ability is hard to control for, simple correlations can overestimate returns to education.

Police and crime

\text{Number of Crimes} = \alpha + \beta \times \text{Size of the Police Force} + U

Cities with more crime often hire more police, so simple correlations can spuriously suggest police increase crime.

--- title: "Lecture 1: Introduction" subtitle: "Economics 326 — Methods of Empirical Research in Economics" author: - name: "Vadim Marmer, UBC" format: html: output-file: 326_01_intro.html toc: true toc-depth: 3 toc-location: right toc-title: "Table of Contents" theme: cosmo smooth-scroll: true html-math-method: katex pdf: output-file: 326_01_intro.pdf pdf-engine: xelatex geometry: margin=1in fontsize: 11pt number-sections: false toc: false revealjs: output-file: 326_01_intro_slides.html theme: solarized smaller: true slide-number: c/t incremental: true html-math-method: katex scrollable: true chalkboard: false self-contained: true --- ## What is econometrics? Econometrics develops **statistical methods** for: - **Estimating** economic relationships - **Testing** economic theories - **Forecasting** important economic variables - **Evaluating** government and business policy ## Why statistics? - Economic theory motivates models of relationships between variables of interest. - Economic **models are approximations**, not exact descriptions of reality. - Even good models omit important factors that affect outcomes. - We replace a **deterministic** model with a **probabilistic** model. ## Examples - Estimation of demand and supply functions Elasticities help evaluate the effects of taxation. - Mincer (1974), *Schooling, Experience, and Earnings* Uses individual data to estimate returns to schooling and experience. - Determine an “optimal” amount of schooling - Study education in developing countries - Study gender and race discrimination - Study the impact of immigration on labour markets - Paarsch (1997), *Journal of Econometrics* Estimates optimal reserve prices for BC timber auctions. - Chandra et al. (2008), *Pediatrics* Studies how exposure to sexual content on TV relates to teen pregnancy. ## Types of data: cross-section ::: {.callout-note title="Definition"} A **cross-sectional** dataset contains observations on individuals (e.g., workers or firms) collected in a single time period. ::: Example (wages and individual characteristics): | obs | wage | education | experience | female | married | |---:|----:|----------:|-----------:|------:|--------:| | 1 | 3.10 | 11 | 2 | 1 | 0 | | 2 | 3.24 | 12 | 22 | 1 | 1 | | 3 | 3.00 | 11 | 2 | 0 | 0 | | … | … | … | … | … | … | - The order of observations is not important. - It is often reasonable to assume observations are **statistically independent**. ## Types of data: time series ::: {.callout-note title="Definition"} A **time series** dataset contains observations on one or more variables over time. ::: Example (Puerto Rico minimum wage, unemployment, and GNP): | obs | year | minimum wage | unemployment | gnp | |---:|----:|-------------:|-------------:|----:| | 1 | 1950 | 0.20 | 15.4 | 878.7 | | 2 | 1951 | 0.21 | 16.0 | 925.0 | | 3 | 1952 | 0.23 | 14.8 | 1015.9 | | … | … | … | … | … | - Data frequency can be daily/weekly/monthly/quarterly/annual; in finance, trade data can be very high frequency. - The order of observations is important. - Observations are often correlated (e.g., trends). ## Types of data: panel ::: {.callout-note title="Definition"} A **panel** dataset combines cross-section and time series: a time series for each cross-sectional unit. ::: Example (two-year panel on city crime): | obs | city | year | murders | population | unempl | police | |---:|----:|----:|--------:|-----------:|------:|-------:| | 1 | 1 | 1986 | 5 | 350000 | 8.7 | 440 | | 2 | 1 | 1990 | 8 | 359200 | 7.2 | 471 | | 3 | 2 | 1986 | 2 | 64300 | 5.4 | 75 | | 4 | 2 | 1990 | 1 | 65100 | 5.5 | 75 | | … | … | … | … | … | … | … | ## Causality - We care about **causal** relationships, but data often only reveal **correlations** (associations). - To claim a causal effect, **other factors** affecting the outcome must be held fixed (controlled for). - Controlled experiments help with causality in the natural sciences. - Experiments are often impossible in economics (cost and ethics). - We typically work with **observational data**. ## Examples (causality) **Education** $$ \log(\text{Wage}) = \alpha + \beta \times \text{Years of Schooling} + U $$ $U$ includes other factors (e.g., **ability**). If ability is hard to control for, simple correlations can **overestimate** returns to education. **Police and crime** $$ \text{Number of Crimes} = \alpha + \beta \times \text{Size of the Police Force} + U $$ Cities with more crime often hire more police, so simple correlations can **spuriously** suggest police increase crime.